Prediction of mixing efficiency of pharmaceutical€¦ · Prediction of mixing efficiency of...

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The Pharma Innovation Journal 2017; 6(8): 328-333

ISSN (E): 2277- 7695

ISSN (P): 2349-8242 NAAS Rating 2017: 5.03 TPI 2017; 6(8): 328-333

© 2017 TPI www.thepharmajournal.com

Received: 22-06-2017

Accepted: 23-07-2017

Preeti

Lloyd Institute of Management

& Technology, Greater Noida,

U.P, Dr. A.P.J. Abdul Kalam

Technical University,

Lucknow, Uttar Pradesh. India

Dr. Vandana Arora






Preeti Maan






Correspondence

Preeti






Prediction of mixing efficiency of pharmaceutical

granules

Preeti, Dr. Vandana Arora and Preeti Maan

Abstract Mixing of ingredients, or dispersion of one phase into another, is an operation widely and most

commonly used in pharmaceutical Industry. It is difficult to find a pharmaceutical product in which

mixing is not done at one stage or the other during its manufacturing. Whenever a product contains more

than one component a mixing or blending stage is must required in the manufacturing process to ensure

even distribution of active ingredients, to ensure an even appearance, to ensure that he dosage form

releases the drug at the correct dose at the desired rate etcA pharmaceutical formulation with low amount

of active ingredient will be in effective or with too high amount of active ingredient may be lethal. So,

mixing is very important process and the final mixture required should be completely mixed and

homogenous for such type of formulations

Keywords: Prediction, pharmaceutical granules, homogenous

Introduction

Mixing is defined as the intermingling of two or more dissimilar portions of materials resulting

in attainment of a desired level of uniformity either physically or chemically or as the process

in which two or more than two components in a separate or roughly mixed condition are

treated in such a way so that each particle of any one ingredient lies as nearly as possible to the

adjacent particles of other ingredients or components. The most important use of mixing is the

production of a homogeneous blend of several ingredients which neutralizes variations in

concentration using the minimum amount of energy and time.

Mechanisms of solid-solid mixing Mixing is defined as the intermingling of two or more dissimilar portions of materials resulting

in attainment of a desired level of uniformity either physically or chemically or as the process

in which two or more than two components in a separate or roughly mixed condition are

treated in such a way so that each particle of any one ingredient lies as nearly as possible to the

adjacent particles of other ingredients or components. The most important use of mixing is the

production of a homogeneous blend of several ingredients which neutralizes variations in

concentration using the minimum amount of energy and time.

Mechanisms of solid-solid mixing The homogeneity of the solid-solid mixing and mixing quality is determined pharmaceutically

by mixing time. The mechanism of mixing process of solids can be of dispersive or convective.

Dispersive mixing is mainly intended for the completely random change of place of the

individual particles. If the ingredients are spatially separated at the beginning of the process,

long times will be required to mix them through dispersive mixing alone, since there is a very

low number of assorted neighbors.

Convective mixing causes a movement of large groups of particles relative to each other and is

also a sort of macro mixing. Convection increases the number of assorted neighbors and

thereby promotes the exchange processes of mixing. A material mass is divided up or

convectively mixed through the rearrangement of a solid‟s layers or by the fall of a stream of

material on another layer. The whole volume of material is continuously divided up into

groups and then mixed again. The forced convection can be achieved by rotating element

paddles. The dimension of the groups, which are composed of just one unmixed ingredient, is

continuously reduced due to splitting action of the rotating paddles.

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The Pharma Innovation Journal

Prediction of mixing efficiency

The variance calculated by using Lacey index (b) is much

easier and simplified to calculate or processed as compared to

other types of variation discussed above. So, that we used the

lacey index equation to determinethe mixing efficiency in our

experimentation work.

In manual procedures, physical sampling of mixture is done

while keeping in mind the 3D aspects of mixture. No doubt,

the maximum accuracy in predicting mixing efficiency can be

obtained by these physical methods, but still these are very

laborious and time consuming procedures. The speed of

prediction of mixing efficiency can be much more increased

by switching from 3D sampling to 2D sampling and that too

in a non-invasive manner. The digital images are among such

2D samples, which can allow mixing efficiency calculations

from surface analysis. The mixing efficiency is predicted in

physical sampling by number fractions or weight fractions.

Similar type of fractions and then variances can be calculated

from 2D surface images also. It is aim of current study to find

out whether the same purpose can be fulfilled by the 2D

surface images or not. Further, in physical sampling, samples

withdrawal manually requires stopping of mixer and causes

disturbance to mixture, sometimes it may also require

complete discharge of mixture from the mixer. These all

difficulties with the 3D sampling can be avoided by using the

2D images. The analysis from images is known as image

analysis and it can be completely automated for mixing

processes and does not causes any disturbance to the mixture.

In addition, image analysis methods offer several advantages

viz storage of data for future access, equal applicability in

small and large processing lots, options for real time studies,

and generating feedback loop system for automatic control of

mixing process. Due to the advantages we have planned to

study image analysis method to predict the mixing efficiency

of solid particles against standard physical sampling method.

Advantage of Image Analysis Image analysis methods offer several advantages viz. storage

of data for future access, equal applicability in small and large

processing lots, options for real time studies, and generating

feedback loop system for automatic control of mixing

process. Due to the advantages we have planned to study

image analysis method to predict the mixing efficiency of

solid particles against Standard physical sampling method.

The images will be taken from the surface of the binary mixer

by a digital camera at various intervals Instead of manual

sampling and will be processed and analysed. It is being

expected that the 2D images can serve the same purpose as

that of manual sampling without any disturbances to the

mixture. For image analysis the particles used for mixing

should be of different colors but should be differentiable, e.g.

color particles. The objective of present study is to calculate

the mixing efficiency by both image analysis and physical

sampling and then comparing them for equivalence studies on

the basis of correlation/similarity.

Work Envisaged

Rational of study

The main objective of this proposed work was to study

mixing efficiency of the mixture of two different colored

pharmaceutical granules by image analysis and physical

counting methods and then to correlate the results of both

methods to examine the usefulness of image analysis method

of predicting mixing efficiency.

Objectives of work

To find out the mixing efficiency of solid/solid mixture by

physical sampling and image analysis method Correlation and

comparison of manual and image analysis technique.

Plan of work

The following plan of work was proposed to achieve desired

goals/objectives.

1. Literature survey

2. Selection of particle material

a) Selection of spherical particles

b) Selection of non-spherical particles

3. Mixing experiment

(a) Mixing experiment by physical sampling

(b) Mixing experiment by image analysis

(c) Mixing experiment by mixed procedure

4. Comparison of methods

(a) Correlation analysis

(b) Optimum mixing time

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(c) Error analysis

(d) Similarity test (f2)

Materials

Black/yellow color mustard seeds were purchased from local

market, Rohtak. Precipitated calcium carbonate was

purchased from Avantor Performance Material Ltd., India.

Shellac flakes, ethyl alcohol were purchased from CDH, New

Delhi, India. Ethyl alcohol used was of analytical grade. All

chemicals used were at least lab grade

Equipments

Sigma mixer Make by Shakti Pharma Tech., Ahmadabad, India

Blender Rolex Double Cone Blender

Sieves Set of sieves from sieve no. 22-30 of BSP

Weighing

balance Analytical weighing balance

Digital

camera

SLR camera fitted with 18-55 mm zoom lens

(make by Sony Nex-5, Thailand)

Selection of mixing particles

As the image analysis method was studied for determining the

mixing efficiency of millimeter sized particles; both spherical

as well as non-spherical particles were considered for mixing

experiments in 1:1 wt% proportion.

Selection of spherical particles

Mustard seeds of two different colors (yellow and brown)

were used as spherical particles because mustard seeds are of

equal and uniform size and can be differentiated from each

other easily by color.

Selection of non- spherical particles

For non-spherical particles, pharmaceutical granules of

calcium carbonate (white and blue) were prepared by wet

granulation method using 30%w/v ethanolic solution of

shellac as granulating agent. The wet mass was passed

through a set of 22-30 BSS sieves and dried in air for 8h. The

dried pharmaceutical granules were further passed through

same sieve set in order to get double refines 22-30 sieve size

granules for mixing experiment.

Mixing Experiments

Mixing experiment by physical sampling

The mixing experiment was carried out in a R&D scale

sigma-mixer (Make: Shakti Pharma Tech, model no. SHMD-

AC, CE) fitted with camera at a fixed length for capturing

images. At time𝑡 = 0𝑠, equal wt. of both yellow and brown

color seeds were placed in two layers depicting total unmixed

state. The mixer was started and operated at a speed of 45 rpm

and sampling was done physically at fixed time intervals of

20s up to 300s and then at every 300s up to 3000s. These

spots were uniformly distributed throughout the mixing

surface, such that adjacent sampling points were not affected

by sampling and were at maximum distance from each other.

After sampling black and yellow particles were counted in

each sample and there fractions were calculated for

calculating further variance values "𝜎𝑟2". The "𝜎0 2" was

calculated from 𝑡 = 0𝑠 readings. After calculating variances,

mixing index "𝑏" was calculated. The experiment was

performed five times in order to report error values. The

experiment was repeated under same conditions for non-

spherical (pharmaceutical granules) particles except for

different sampling time intervals, i.e. 2s up to 20s, then 4s up

to 60s, followed by 8s up to 100s and 12s up to 160s and at

last 16s up to 208s.

Sampling points in the Sigma-arm mixer

Mixing experiment by image analysis

As the mixer was fitted with SLR camera, so images were

also captured at all sampling time points just before physical

sampling. Thus for the mixing analysis, sampling was done

by images at same time points used for physical sampling.

The camera was pre setted at; focal length of 4.5mm, shutter

speed of 1/8s and at a fixed distance of 1.5 feet from mixture

surface. The resolution of each digital image was 4592×3056

pixels. After taking images, images were processed through

various steps to finally calculate the value of mixing index

“bIA”. Steps adopted for calculating mixing index were:

Image processing and “Mixing index” calculations

Image J 1.34S software of the National Institute of Health,

USA was used for processing of images of mixture [5]. On

each image, sampling spots as shown in Fig.3 were selected

and then converted into binary spot images. The black and

white area fractions were calculated using “set measurement”

command of software under “Analyze tab”. The "𝜎𝑟2" and

"𝜎02" were calculated using area fractions followed by

calculation of “bIA”. The results were reported as average

“bIA” values vs. time. The flow chart of procedure for digital

image processing for area fraction measurements is given

below

Mixing experiment by mixed method

In addition to physical sampling and image analysis,

intermediate method involving manual counting of different

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particles from digital images was also carried out. Mixing

index “bMM” was calculated on the basis of numbers of

particles counted from image manually. This also resulted

into a number based Mixing index “bMM” and the results

were reported as “bMM” value vs. time.

Comparison of methods

Correlation analysis

Various “b” values calculated above by physical sampling, mixed

method and image analysis were correlated with each other for

explaining the usefulness of techniques. Among these methods physical

sampling was the reference method and image analysis was the test

method. The correlation coefficients (r) were calculated for the “bi”

values obtained for all the three methods. A value of correlation

coefficient greater than 0.9 was considered as a highest level of

correlation between the methods.

Similarity test (f2)

A similarity test f2 was applied for comparison of data sets obtained for

various methods. The f2 value is a point-to-point comparison of values at

common time points. The method is based on calculation of residual

values and gives quantitative comparison instead of qualitative or

graphical comparison. The general equation for f2 value is represented as

follows:

Where n is no. of samples, r is reference “bPS” value and t are test “bi”

values. The f2 value˃50 describes the similarity of two data sets while

the value˂50 indicates dissimilarity between data sets.

Optimum mixing time

The optimum mixing times were predicted by tangents intersection

point in “bi” vs. time graphs for both spherical and non-spherical

particles. The intersection point described the time, where mixing is

maximum or variation is minimum. So, comparison of optimum

mixing times from various methods acted as a parameter for setting

usefulness or non-usefulness of test methods.

Error analysis

The %CV was calculated from repeated experiments using formula

given as:

Where SD = Standard Deviation.

Result & Discussion

Mixing by physical sampling

The data set of mixing index (bPS) for five independent

mixing experiments involving spherical and non spherical

particles have been given in Table 5.1 and 5.2. The value of

mixing index "𝑏" are ˃1 for spherical particles and ˂ 1 for

granules or non-spherical (granules) particles. This variation

in standard deviation (SD) (Fig. 5.1 & 5.2) for spherical

particles is almost half as that for non-spherical particles. The

difference may be spotted due to the lackness of sphericity in

granules as compared to spherical particles, which might have

created difficulty in mixing in case of non-spherical particles.

Mixing by mixed method The data set of mixing index “bMM” for five independent

mixing experiments involving spherical and nonspherical

particles have been given in Table 5.4 and 5.5. The value of

mixing index “bMM” are ˃1 for spherical particles and ˂ 1

for non-spherical (pharmaceutical granules) particles.. The

difference in SD is not so large, hence, both spherical and

non-spherical mixings are appearing similarly predicted by

mixedmethod.

Mixing by image analysis

The data sets of mixing index “bIA” for five independent

mixing experiments involving spherical and nonspherical

particles have been given in Table 5.5 and 5.6. The values of

mixing index “bIA” are ˃1 for both spherical and non-

spherical particles. The SD values (Fig. 5.7 & 5.8) are

somewhat larger for non-spherical particles than spherical

particles, however the difference can be considered small as

compared to original values at this high significant figures

level.

Comparison of methods

Correlation analysis The correlation coefficients “r” between various methods for

spherical particles have been given in Table 5.7. The “r” was

found to be 0.5 between image analysis and mixed method

while the “r” value was >0.9 for both physical sampling vs

image analysis and physical sampling vs mixed method. This

shows that standard physical sampling method and image

analysis method are equivalent with each other For non-

spherical, correlation coefficient “r” was found to be >0.9

between all methods indicating good correlation between all

methods. On an overall, the value of “r” was greater for the

image analysis method as compared to mixed method, so

image analysis is more similar/equivalent to standard method

and can be used for predicting mixing efficiencies.

Optimum mixing time

Mixing time for spherical particles

The values of optimum mixing time for spherical particles

was found to be about 70s, 60s & 70s by physical sampling,

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mixed method and image analysis method. The mixing time is

quite similar for standard physical method and image analysis

method.

Optimum mixing time by mixed method for spherical particles

Mixing time for non-spherical particles The values of optimum mixing time for spherical particles

was found to be about 24s, 12s & 15s by physical sampling,

mixed method and image analysis method (Fig. 5.12, 5.13 &

5.14) respectively. The mixing time predicted by image

analysis and mixed method is appearing half as that predicted

by physical method but still optimum mixing time by image

analysis is more near to that predicted by standard physical

sampling. Therefore, image analysis is again better method,

which can gives result near to standard method.

Error analysis

The graphical plot of %CV for all the three methods have

been shown in Fig. 5.15 and 5.16 for spherical and non-

spherical particles respectively. The variation in %CV of

image analysis method is least among all for both spherical

and non-spherical particles. This indicated stability of image

analysis towards predicting mixing efficiency. Further the

profile of %CV of image analysis is matching with physical

sampling indicating similarity of image analysis method with

standard method. The variations in mixed method were

deviating by large, so method was not considered useful.

Fig. 5.3 % Coefficient of variation (%CV) of mixing indices for

spherical particles by various methods

Fig 5.4 % Coefficient of variation (%CV) of mixing indices for non-

spherical particles by various methods

Similarity test (f2) analysis

The similarity factors (f2) between various methods for

spherical and non-spherical particles have been given in Table

5.9 & 5.10. The f2 value were found to be >50 for all

methods. This shows similarity between all methods. On an

overall, the “f2” value was greater for the image analysis

method as compared to mixed method for both spherical and

non-spherical particles, so image analysis is maximum useful

for predicting mixing efficiencies.

Summary & Conclusion

In this work, the mixing efficiency of solid-solid mixing of

two different color particles by means of the mixing index at

different time intervals was calculated. For this particular

purpose, Mixing index was calculated by using different

methods i.e. physical sampling, mixed method and image

analysis method. All the methods used for predicting mixing

efficiency was compared and correlated with each other.

The two types of particles; spherical and non-spherical

particles were taken for mixing experiment. Mustard seeds

(yellow and brown color) were selected as spherical particles

and pharmaceutical granules (white and blue color) were used

as non-spherical particles. Methylene blue dye was blended

with calcium carbonate blue color pharmaceutical granules.

The mixing experiment was firstly done by spherical particles

in a sigma-arm mixer. The speed of mixer was fixed at 45

rpm and sampling was done at prefixed time points. The

samples were taken from five different locations within the

mixer. The experiment was repeated five times for average

value. The above experiment was also performed separately

for non-spherical particles in a similar manner as that for

spherical particles.

The no. based fraction of particles were calculated for

physical samples and mixed methods. The area fraction of

particles was measured with the help of Image J software for

image analysis method. From the above fractions, the mixing

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index “bi” were calculated and used for relating different

methods.

All the methods used for mixing efficiency prediction were

compared with each other by means of Correlation analysis,

optimum mixing time calculation, error analysis and

similarity test applied on “bi” values. For spherical particles,

the value of correlation coefficient “r” was >0.9 for physical

sampling vs image analysis and physical sampling vs mixed

method and 0.5 for image analysis and mixed method. For

non-spherical particles, the value of correlation coefficient “r”

was found to be >9 between all the methods.

The highest values of “r”were found for physical sampling vs

image analysis. Hence, image analysis can be used instead of

mixed method.

The mixing time calculated from tangent intersection points

showed that in case of spherical particles, as well as non-

spherical particles, image analysis was the For spherical and

non-spherical particles, the %CV was found to be least for

image analysis method. The variations in mixed method were

deviating by large, so this method was not recommended.

The values of similarity factor “f2” between all the methods

were found to be > 50 for both spherical and non-spherical

particles which showed the similarity between methods. The

image analysis method showed highest similarity with

standard method (physical sampling), hence finished for

considering as replacement for standard physical mixing

method.

It is concluded that the image analysis method is similar to

standard physical sampling method in predicting mixing

efficiency. So, we can use the image analysis in place of

physical sampling method. The use of image analysis in place

of physical sampling makes the experiment very simple and

easy and non-invasive.

In addition to this, image analysis having so many other

advantages over physical sampling makes itself a best

method.

Acknowledgements

During my course of work to fulfil the 127 requirements of

this project, I have been 128 accompanied and supported by

many people. I 129 am sincerely thankful to each and every

person 130 who extended their support to me in terms of 131

their knowledge that has been a guiding source 132 for me. I

am highly grateful to my 133 supervisor Dr. Vandana Arora,

Lecturer, 134 Lloyd Institute of Management and 135

Technology, Greater Noida, for her continued 136 support

and valuable suggestions throughout 137 my M.Pharm

education, especially during my 138 project that enabled me

to complete my thesis. 139 It is my privilege to express

humble regards 140 to Jubilant Generics Ltd., R&D Centre,

141 Noida, whose expertise helped me to carry out 142 my

project successfully.

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